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Designing versatile graph learning approaches is important, considering the diverse graphs and tasks existing in real-world applications.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Autocross: Automatic feature crossing for tabular data in real-world applications. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1936–1945
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. 2019 · 1945
Earlier work this paper cites.
Autofield: Automating feature selection in deep recommender systems. In Proceedings of the ACM Web Conference 2022 . 1977–1986
Yejing Wang, Xiangyu Zhao, Tong Xu, and Xian Wu. 2022c · 1986
Earlier work this paper cites.
Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig. 2003 · 2003
Earlier work this paper cites.
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification. In ICDM . 678–689
N Wale and G Karypis. 2006 · 2006
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Recommender systems
Linyuan Lü, Matúš Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou. 2012 · 2012
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning . PMLR, 115–123
James Bergstra, Daniel Yamins, and David Cox. 2013 · 2013
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web . 507–517
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019 · 2017
Earlier work this paper cites.
Neural Message Passing for Quantum Chemistry. In ICML . 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs. In NeurIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le. 2017 · 2017
Earlier work this paper cites.
Efficient Neural Architecture Search via Parameter Sharing. In ICML . 4092–4101
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. 2018 · 2018
Earlier work this paper cites.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Representation learning on graphs with jumping knowledge networks. In ICML . 5453–5462
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Taking human out of learning applications: A survey on automated machine learning
Quanming Yao, Mengshuo Wang, Yuqiang Chen, Wenyuan Dai, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, and Yang Yu. 2018 · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling. In NeurIPS . 4800–4810
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
An end-to-end deep learning architecture for graph classification. In AAAI
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018 · 2018
Earlier work this paper cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
Graphnas: Graph neural architecture search with reinforcement learning. In IJCAI
Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020 · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
AutoGraph: Automated Graph Neural Network. In ICONIP . 189–201
Yaoman Li and Irwin King. 2020 · 2020
Earlier work this paper cites.
Automated embedding size search in deep recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 2307–2316
Haochen Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, and Jiliang Tang. 2020 · 2020
Earlier work this paper cites.
Sign: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti. 2020 · 2020
Cited alongside, same era.
Design Space for Graph Neural Networks. In NeurIPS
Jiaxuan You, Rex Ying, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Simplifying Architecture Search for Graph Neural Network
Huan Zhao, Lanning Wei, and Quanming Yao. 2020 · 2020
Cited alongside, same era.
Beyond homophily in graph neural networks: Current limitations and effective designs. In NeurIPS
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020 · 2020
Cited alongside, same era.
Adaptive universal generalized pagerank graph neural network
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021 · 2021
Cited alongside, same era.
Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023 · 2023
Closest in time.
Text Classification via Large Language Models
Xiaofei Sun, Xiaoya Li, Jiwei Li, Fei Wu, Shangwei Guo, Tianwei Zhang, and Guoyin Wang. 2023b · 2023
Closest in time.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, et al · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
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Mixed dimension embeddings with application to memory-efficient recommendation systems. In 2021 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2786–2791
Antonio A Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, and James Zou. 2021 · 2021
Cited alongside, same era.
New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim. 2021 · 2021
Cited alongside, same era.
Learning to pre-train graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 4276–4284
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. 2021 · 2021
Cited alongside, same era.
Automated Machine Learning on Graphs: A Survey
Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2021 · 2021
Cited alongside, same era.
Search to aggregate neighborhood for graph neural network. In ICDE
Huan Zhao, Quanming Yao, and Weiwei Tu. 2021 · 2021
Cited alongside, same era.
Autoemb: Automated embedding dimensionality search in streaming recommendations. In 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 896–905
Xiangyu Zhaok, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Xiwang Yang. 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Cited alongside, same era.
Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, and Jiajun Bu. 2023a · 2023
Closest in time.
A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al · 2023
Closest in time.
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. In Proceedings of the ACM Web Conference 2023 . 588–598
Lanning Wei, Zhiqiang He, Huan Zhao, and Quanming Yao. 2023 · 2023
Closest in time.
LLM Powered Autonomous Agents
Lilian weng. June 23, 2023 · 2023
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GPT-NAS: Neural Architecture Search with the Generative Pre-Trained Model
Caiyang Yu, Xianggen Liu, Chenwei Tang, Wentao Feng, and Jiancheng Lv. 2023 · 2023
Closest in time.
TaskLAMA: Probing the Complex Task Understanding of Language Models
Quan Yuan, Mehran Kazemi, Xin Xu, Isaac Noble, Vaiva Imbrasaite, and Deepak Ramachandran. 2023 · 2023
Closest in time.
Building Cooperative Embodied Agents Modularly with Large Language Models
Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B Tenenbaum, Tianmin Shu, and Chuang Gan. 2023a · 2023
Closest in time.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT
Jiawei Zhang. 2023 · 2023
Closest in time.
AutoML-GPT: Automatic Machine Learning with GPT
Shujian Zhang, Chengyue Gong, Lemeng Wu, Xingchao Liu, and Mingyuan Zhou. 2023b · 2023
Closest in time.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, et al · 2023
Closest in time.
Can GPT-4 Perform Neural Architecture Search?
Mingkai Zheng, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu, and Samuel Albanie. 2023 · 2023
Closest in time.
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, and Jun Wang. 2024 · 2024
Closest in time.
Metagpt: Meta programming for multi-agent collaborative framework
Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al · 2024
Closest in time.
Planbench: An extensible benchmark for evaluating large language models on planning and reasoning about change
Karthik Valmeekam, Matthew Marquez, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2024 · 2024
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2024 · 2024
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2024a · 2024
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Large language models can learn temporal reasoning
Siheng Xiong, Ali Payani, Ramana Kompella, and Faramarz Fekri. 2024b · 2024
Closest in time.
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. 2024 · 2024
Closest in time.
Pooling architecture search for graph classification. In CIKM . 2091–2100
Lanning Wei, Huan Zhao, Quanming Yao, and Zhiqiang He. 2021 · 2091
Closest in time.
Graph u-nets
Hongyang Gao and Shuiwang Ji. 2019 · 2092
Closest in time.